A semantic enterprise search seeks to add context to a search that may touch data available to an enterprise. The context may concern, for example, a user task, an enterprise task, a user identity, a user location, and so on. One way to reach data available to an enterprise is by using a crawler. The crawler may traverse an enterprise, seek out is data, and create and maintain an index to the data it discovers. Conventionally, a crawler may touch both structured and unstructured data.
Unstructured data may reside in unstructured document repositories including, for example, the Web, Intranets, content management systems, record management systems, email systems, and so on. Data may also reside in repositories that have rich structural information available (e.g., applications). That information may be under-utilized, if utilized at all.
A search engine may typically be used to access both the structured and unstructured data. The search engine may use the index created by the crawler. A search engine is typically tasked with retrieving lists of potentially relevant documents, leaving a user to manually analyze the potentially relevant documents and to identify knowledge of interest.
Searching may include examining metadata associated with documents. Metadata may be added automatically and/or manually to documents. This metadata may include scarce original metadata (e.g., author, title, creation date) and subsequently created metadata. Data for automatic metadata annotations may be identified by Information Extraction (IE) logic. Conventional IE logic may be tasked with identifying, for example, a document topic, a document theme, key concepts, most frequently used sentence subject, and so on. While IE and metadata annotations may have improved conventional searching, true semantic enterprise searching may not have been achieved.